Preferences for structured reporting of measurement data: an institutional survey of medical oncologists, oncology registrars, and radiologists.

RATIONALE AND OBJECTIVES: The aim of this study was to determine whether key radiology report "consumers" in our institution prefer structured measurement reporting in a dedicated report section over the current practice of embedding measurements throughout the "Findings" section, given the availability of new tools for quantitative imaging interpretation that enable automated structured reporting of measurement data. MATERIALS AND METHODS: Oncologic clinicians and radiologists at our institution were surveyed regarding their preferences for a standard report versus three reports each having uniquely formatted dedicated "Measurements" sections and regarding their impressions of various characteristics of report quality demonstrated by these reports. The online survey was completed by 25 radiologists, 16 oncologists, and 17 oncology nurses and research assistants (registrars). RESULTS: Aggregation of respondents' preferences by group into single orderings using the Kemeny-Young method revealed that both oncology groups preferred all proposed reports to the standard report but that radiologists only preferred two of the proposed reports to the standard report. All preferences for proposed reports in the two oncology groups were statistically significant based on Wilcoxon tests, but the preference for only one of the proposed reports was significant for radiologists. Additional results suggest that these preferences are driven by respondent favor for the readability of and confidence conveyed by the proposed reports compared to the standard report. CONCLUSIONS: Oncologic clinicians responding to our survey preferred communication of lesion measurements in a separate report section to the current practice of embedding measurements throughout the "Findings" section, based on their assessments of reports containing simulated measurement sections assembled from a single sample report using standardized formatting.

Computed tomography pulmonary angiography in acute pulmonary embolism: the effect of a computer-assisted detection prototype used as a concurrent reader.

PURPOSE: To assess the effect of computer-assisted detection (CAD) on diagnostic accuracy, reader confidence, and reading time when used as a concurrent reader for the detection of acute pulmonary embolism in computed tomography pulmonary angiography. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, 6 observers with varying experience evaluated 158 negative and 38 positive consecutive computed tomography pulmonary angiographies (mean patient age 60 y; 115 women) without and with CAD as a concurrent reader. Readers were asked to determine the presence of pulmonary embolism, assess their diagnostic confidence using a 5-point scale, and document their reading time. Results were compared with an independent standard established by 2 readers, and a third chest radiologist was consulted in case of discordant findings. RESULTS: Using logistic regression for repeated measurements, we found a significant increase in readers' sensitivity (P<0.001) without loss of specificity (P=0.855) with the effects being reader dependent (P<0.001). Sensitivities varied from 68% to 100% without CAD and from 76% to 100% with CAD. A 2-way analysis of variance showed a small but significant decrease in reading time (P<0.001), with the duration varying between 24 and 208 seconds without CAD and between 17 and 196 seconds with CAD, and a significant increase in readers' confidence scores using CAD as a concurrent reader (P<0.001). CONCLUSIONS: CAD as a concurrent reader has the potential to increase readers' sensitivity and confidence with a decrease in reading time without loss of specificity. The differences between readers, however, require further evaluation of CAD as a concurrent reader in a larger trial before stronger conclusions can be drawn.

Acute pulmonary embolism: effect of a computer-assisted detection prototype on diagnosis--an observer study.

PURPOSE: To assess the effect of a computer-assisted detection (CAD) prototype on observer performance for detection of acute pulmonary embolism (PE) with computed tomographic (CT) pulmonary angiography. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, six observers with varying experience evaluated 158 PE-negative and 51 PE-positive CT pulmonary angiographic studies (mean age, 57 years; 111 women, 98 men) obtained consecutively during nights and weekends. Observers were asked to determine the presence of PE and to rank their diagnostic confidence without CAD and subsequently with CAD within a single reading session. Reading time was separately measured for both readings. Reader data were compared with an independent standard established by two readers, with a third in case of discordant results. Statistical evaluation was performed on a per-patient basis by using logistic regression for repeated measurements and Pearson correlation. RESULTS: With CAD, there was a significant increase in readers' sensitivity (P = .014) without loss of specificity (P = .853) on a per-patient basis. CAD assisted the readers in correcting an initial false-negative diagnosis in 15 cases, with the most proximal embolus at the segmental level in four cases and at the subsegmental level in 11 cases. In eight cases, readers accepted false-positive CAD candidate lesions on scans negative for PE, and in one case, a reader dismissed a true-positive finding. Reading time was extended by a mean of 22 seconds with the use of CAD. CONCLUSION: At the expense of increased reading time, CAD has the potential to increase reader sensitivity for detecting segmental and subsegmental PE without significant loss of specificity.

Impact of image quality on the performance of computer-aided detection of pulmonary embolism.

OBJECTIVE: The purpose of this article is to assess the relationship between CT image quality and the number and type of false-positive (FP) findings found by a prototype computer-aided detection (CAD) algorithm for automatic detection of pulmonary embolism (PE). MATERIALS AND METHODS: This retrospective study included 278 subjects (138 men and 140 women; mean age, 57 years; range, 18-88 years) who underwent consecutive CT pulmonary angiographies performed during off hours. Twenty-four percent (68/278) of studies were reported as positive for PE. CAD findings were classified as true-positive or FP by two independent readers and, in cases of discordance, by a third radiologist. Each FP result was classified according to underlying cause. The degree of vascular enhancement, image noise, motion artifacts, overall quality, and presence of underlying lung disease were rated on a 4- or 5-point scale. Chi-square tests and t tests were used to test significance of differences. RESULTS: The mean number of FP CAD findings was 4.7 (median, 2) per examination. Most were caused by veins (30% [389/1,298]) or airspace consolidations (22% [286/1,298]). There was a significant positive association between the number of FP findings and image noise, motion artifacts, low vascular enhancement, low overall quality, and the extent of underlying disease. On a per-embolism basis, sensitivity decreased from 70.6% (214/303) for scans with zero to five FP findings, to 62.3% (33/53) for scans with six to 10 FP findings, to 60% (12/20) for scans with more than 10 FP findings. CONCLUSION: There is a strong association between CT image quality and the number of FP findings indicated by a CAD algorithm for the detection of PE.

Computer-assisted detection of pulmonary embolism: evaluation of pulmonary CT angiograms performed in an on-call setting.

PURPOSE: The purpose of the study was to assess the stand-alone performance of computer-assisted detection (CAD) for evaluation of pulmonary CT angiograms (CTPA) performed in an on-call setting. METHODS: In this institutional review board-approved study, we retrospectively included 292 consecutive CTPA performed during night shifts and weekends over a period of 16 months. Original reports were compared with a dedicated CAD system for pulmonary emboli (PE). A reference standard for the presence of PE was established using independent evaluation by two readers and consultation of a third experienced radiologist in discordant cases. RESULTS: Original reports had described 225 negative studies and 67 positive studies for PE. CAD found PE in seven patients originally reported as negative but identified by independent evaluation: emboli were located in segmental (n = 2) and subsegmental arteries (n = 5). The negative predictive value (NPV) of the CAD algorithm was 92% (44/48). On average there were 4.7 false positives (FP) per examination (median 2, range 0-42). In 72% of studies or=10 FP. CONCLUSION: CAD identified small emboli originally missed under clinical conditions and found 93% of the isolated subsegmental emboli. On average there were 4.7 FP per examination.

Computer-aided detection of polyps in CT colonography using logistic regression.

We present a computer-aided detection (CAD) system for computed tomography colonography that orders the polyps according to clinical relevance. The CAD system consists of two steps: candidate detection and supervised classification. The characteristics of the detection step lead to specific choices for the classification system. The candidates are ordered by a linear logistic classifier (logistic regression) based on only three features: the protrusion of the colon wall, the mean internal intensity, and a feature to discard detections on the rectal enema tube. This classifier can cope with a small number of polyps available for training, a large imbalance between polyps and non-polyp candidates, a truncated feature space, unbalanced and unknown misclassification costs, and an exponential distribution with respect to candidate size in feature space. Our CAD system was evaluated with data sets from four different medical centers. For polyps larger than or equal to 6 mm we achieved sensitivities of respectively 95%, 85%, 85%, and 100% with 5, 4, 5, and 6 false positives per scan over 86, 48, 141, and 32 patients. A cross-center evaluation in which the system is trained and tested with data from different sources showed that the trained CAD system generalizes to data from different medical centers and with different patient preparations. This is essential to application in large-scale screening for colorectal polyps.